This paper studies the problem of cross-network node classification to
overcome the insufficiency of labeled data in a single network. It aims to
leverage the label information in a partially labeled source network to assist
node classification in a completely unlabeled or partially labeled target
network. Existing methods for single network learning cannot solve this problem
due to the domain shift across networks. Some multi-network learning methods
heavily rely on the existence of cross-network connections, thus are
inapplicable for this problem. To tackle this problem, we propose a novel
\textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the
techniques of adversarial domain adaptation and graph convolution. It consists
of two components: a semi-supervised learning component and an adversarial
domain adaptation component. The former aims to learn class discriminative node
representations with given label information of the source and target networks,
while the latter contributes to mitigating the distribution divergence between
the source and target domains to facilitate knowledge transfer. Extensive
empirical evaluations on real-world datasets show that AdaGCN can successfully
transfer class information with a low label rate on the source network and a
substantial divergence between the source and target domains. The source code
for reproducing the experimental results is available at
this https URL

本文研究跨网络节点分类问题，提出一种基于对抗领域自适应和图卷积的图传输学习框架 AdaGCN 以在标签数据不足的情况下，从部分标记的源网络中利用标记信息辅助完全未标记或只有部分标记的目标网络节点分类，并在真实数据集上进行了大量实验评估，并验证了 AdaGCN 在标签率低和源目标领域差异显著的情况下都能成功传输类信息。